WO2022100520A1 - Logiciel d'application informatique et système de données de gestion des voies respiratoires pour la prédiction de difficultés au niveau des voies respiratoires - Google Patents

Logiciel d'application informatique et système de données de gestion des voies respiratoires pour la prédiction de difficultés au niveau des voies respiratoires Download PDF

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Publication number
WO2022100520A1
WO2022100520A1 PCT/CN2021/128907 CN2021128907W WO2022100520A1 WO 2022100520 A1 WO2022100520 A1 WO 2022100520A1 CN 2021128907 W CN2021128907 W CN 2021128907W WO 2022100520 A1 WO2022100520 A1 WO 2022100520A1
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airway
difficult
patient
unit
prediction
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PCT/CN2021/128907
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English (en)
Chinese (zh)
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姚卫东
王斌
吴玥
魏铁钢
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安徽玥璞医疗科技有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

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  • the invention relates to the technical field of clinical medicine, in particular to a computer application software and an airway management data system for predicting difficult airways.
  • Difficult airway conditions refer to situations in which artificial ventilation is required due to illness or surgery during general anesthesia, emergency treatment, and diagnosis and treatment of critically ill patients, but difficulties are encountered in establishing an artificial ventilation channel.
  • Clinically difficult airway conditions include difficulty in mask ventilation, difficulty in revealing the glottis by laryngoscopy, difficulty in intubation, intubation failure, and difficulty in supraglottic airway ventilation, whether rescue intubation in critically ill patients or tracheal intubation after induction of general anesthesia All patients have lost their normal respiratory function. Once a patient cannot be successfully intubated in time and cannot be effectively ventilated, hypoxia will occur quickly. Just a few minutes of hypoxia may cause irreversible brain damage, directly endangering If a surgical airway cannot be established urgently, cricothyroidotomy or tracheotomy is performed, the patient will suffer from cardiac arrest, brain injury, and even death due to asphyxia. Therefore, it is particularly important to predict the difficult airway. It is necessary to check the patient before to identify whether there is a danger, and the process of predicting the difficult airway should not be cumbersome, and it is necessary to save time and deal with it in advance.
  • the difficult airway is formed by many factors, but the anatomical composition principle of the difficult airway has not been proved;
  • the purpose of the present invention is to provide a computer application software and an airway management data system for predicting difficult airways, so as to solve the problems raised in the above background art.
  • the present invention provides the following technical solutions:
  • a computer application software and an airway management data system for predicting difficult airways including a medical staff application module and a central control module, wherein the medical staff application module includes a difficult airway geometry computer simulation program unit, a difficult airway multi-data machine Learning prediction model program unit, difficult airway face recognition machine learning prediction model program unit, image information input and processing unit, prediction result output unit,
  • the difficult airway geometry computer simulation program unit is responsible for the extraction of upper airway anatomical feature points, the coordinate positioning simulation of the feature points, the movement law and trajectory of the anatomical feature points when the glottis is exposed by the laryngoscope, the parameter changes of the difficult airway patients, the parameters
  • the geometric interaction relationship and the mechanical interaction relationship between the two parameters are the rotation and displacement of each anatomical feature point when the laryngoscope exposes the glottis, so as to establish a geometric anatomical model of the upper airway.
  • the data machine learning prediction model program unit is responsible for using machine learning technology to establish an artificial intelligence prediction model program
  • the difficult airway face recognition machine learning prediction model program unit is responsible for using machine learning to establish a difficult airway face recognition artificial intelligence prediction model program
  • the The image information input and processing unit is responsible for calculating and processing the necessary patient information uploaded to the central server
  • the prediction result output unit is responsible for outputting the calculation results to the central database and the display and memory of the user terminal
  • the central control module includes a difficult airway prediction procedure performance optimization unit, and the difficult airway prediction procedure performance optimization unit is responsible for selecting the difficult airway procedure.
  • the difficult airway prediction includes the following steps:
  • S1 Construct the geometric analysis theory of the upper airway in the difficult airway. Through the geometric model, analyze the geometric principle of the anatomical features of the upper airway to form the difficult airway, and reconstruct the upper airway according to the anatomical characteristics of the patient's upper airway Two-dimensional schematic diagram of airway anatomy;
  • anatomical feature points include head, neck, The body of the tongue, mandible, pharynx, larynx, and their accessory tissues and boundary points;
  • S3 According to a certain amount of sample measurement data, determine the coordinates and dimensions of each anatomical feature point of the upper airway of the patient and the determinants of these dimensions, establish an anatomical simulation coordinate system of the upper airway, and then analyze the anatomy related to the formation of the difficult airway.
  • the feature points are located in the coordinate system;
  • the rotation amount and displacement amount of each anatomical feature point include: head rotation amount, direction angle and distance of mandibular advancement, glottis displacement angle and distance, and tongue compression direction angle and distance.
  • the image information input and processing unit calculates and analyzes the data and images uploaded to the central server, and for the patient's facial image information, the facial recognition program is preferentially called to identify the patient's eyes, and the code mosaic is used to hide the identifiable patient. to store, calculate, and analyze private information.
  • the output result of the prediction result output unit includes the simulation output of the image, the output of the calculation result of the glottal field of view, and the credible range of the value based on big data statistics.
  • the medical staff module also includes a software startup unit, a registration login unit, a home page interface design unit, a patient information input unit, and a first information management and retrieval unit,
  • the patient information input unit is logged in by the user through the real-name authentication, and the necessary information of the patient is input, and the necessary information includes the patient's age, gender, height, weight, mouth opening, nail-mental distance, tongue-chin distance, tongue.
  • Users can also input the patient’s actual surgical process data, such as whether there is a difficult airway,
  • the type, degree, and processing results of difficult airways are stored in the central server database, and each input box is followed by helpful information to guide users to standard data collection methods.
  • the central control module further includes a user registration authentication unit, a user management authorization unit, and a second information management and retrieval unit.
  • the user can modify and delete the patient data managed by himself, and can further retrieve and classify the patient data, and can also generate the patient's case report form.
  • Authorization to customize their own data category catalogue The system administrator can set various parameters of the system through the central control module, including user personnel management, authorized project content, calling program unit selection and mode selection, and the mode includes clinical application. Mode and scientific research mode, the system administrator can select, replace, update and optimize the difficult airway program through the central control module, and the system administrator can retrieve, classify, delete, backup, edit and analyze the data stored in the server.
  • the present invention has the following beneficial effects: the present invention is based on the inherent mechanism of difficult airway formation, thereby improving the prediction accuracy; Factor judgment process; through computer graphics computing technology, modeling and simulation, high detection authenticity; through artificial intelligence machine learning of large sample difficult airway clinical data and difficult airway facial feature data, respectively establish difficult airway artificial intelligence prediction model program And difficult airway facial recognition model program, which further improves the prediction performance; based on the core function model programming, it is convenient for clinical application; the big data management function is convenient for users to carry out corresponding scientific research work.
  • FIG. 1 is a block diagram of a computer application software and an airway management data system for predicting difficult airways of the present invention
  • FIG. 2 is a flowchart of a computer application software and an airway management data system for predicting a difficult airway according to the present invention
  • FIG. 3 is a coordinate diagram of anatomical feature points of a computer application software for predicting difficult airways and an airway management data system of the present invention
  • FIG. 4 is a schematic diagram of a home page interface of a computer application software for predicting difficult airways and an airway management data system according to the present invention
  • FIG. 5 is a schematic diagram of an information input interface of a computer application software for predicting difficult airways and an airway management data system according to the present invention
  • Figure 6 is a schematic diagram of the result output interface of a computer application software for predicting difficult airways and an airway management data system of the present invention.
  • a computer application software and an airway management data system for predicting a difficult airway including a medical staff application module and a central control module, the medical staff application module including a difficult airway geometry computer simulation program unit, a difficult airway multi-data machine learning prediction Model program unit, difficult airway face recognition machine learning prediction model program unit, image information input and processing unit, prediction result output unit, software startup unit, registration login unit, home page interface design unit, patient information input unit and first information management and retrieval unit.
  • the computer simulation program unit of difficult airway geometry is responsible for the extraction of upper airway anatomical feature points, the coordinate positioning simulation of feature points, the movement law and trajectory of anatomical feature points when laryngoscope exposes the glottis, the parameter changes of patients with difficult airway, and the relationship between parameters.
  • Geometric interaction relationship and mechanical interaction relationship the parameters are the rotation and displacement of each anatomical feature point when the laryngoscope exposes the glottis, so as to establish the upper airway geometric anatomical model, and multi-data machine learning prediction of difficult airways
  • the model program unit is responsible for using machine learning technology to establish an artificial intelligence prediction model program
  • the difficult airway face recognition machine learning prediction model program unit is responsible for using machine learning to establish an artificial intelligence prediction model program for difficult airway face recognition
  • the image information input and processing unit is responsible for The necessary patient information uploaded to the central server is processed for calculation
  • the prediction result output unit is responsible for outputting the calculation results to the central database and the display and memory of the user terminal.
  • the central control module includes a difficult airway prediction program performance optimization unit, a user registration authentication unit, a user management authorization unit, and a second information management and retrieval unit.
  • the difficult airway prediction program performance optimization unit is responsible for selecting difficult airway programs.
  • the difficult airway prediction includes the following steps:
  • S1 Construct the geometric analysis theory of the upper airway in the difficult airway. Through the geometric model, analyze the geometric principle of the anatomical features of the upper airway to form the difficult airway, and reconstruct the upper airway according to the anatomical characteristics of the patient's upper airway Two-dimensional schematic diagram of airway anatomy;
  • anatomical feature points include head, neck, tongue, jaw, pharynx, larynx;
  • S3 According to a certain amount of sample measurement data, determine the coordinates and dimensions of each anatomical feature point of the upper airway of the patient and the determinants of these dimensions, establish an anatomical simulation coordinate system of the upper airway, and then analyze the anatomy related to the formation of the difficult airway.
  • the feature points are located in the coordinate system;
  • the rotation amount and displacement amount of each anatomical feature point in step S5 include: the rotation amount of the head, the direction angle and distance of mandibular advancement, the glottis displacement angle and distance, the tongue compression direction angle and distance, the parameter regression equation
  • the example is as follows:
  • y is the calculated parameter value, that is, the direction or amount of the final displacement of the corresponding anatomical feature point
  • x is the input variable, that is, the clinical detection value
  • a and b are the adjustment coefficients and constant terms of the equation, and their values depend on the clinical data. The statistical results of this equation will determine the displacement trajectory of each anatomical feature point, and each parameter in it has the function of iterative modification and optimization.
  • the patient information input unit allows the user to enter the necessary information of the patient after logging in through real-name authentication.
  • the necessary information includes the patient's age, gender, height, weight, mouth opening, nail-chin distance, tongue-chin distance, tongue thickness, and temporomandibular joint activity. degree, the patient's face frontal photo, and the lateral head-up sniffing position photo, and upload them to the central server.
  • the user can also input the patient's real surgical process data, such as whether there is a difficult airway, and the type and degree of the difficult airway. As well as process the results and store them in a central server database.
  • the image information input and processing unit calculates and analyzes the data and images uploaded to the central server.
  • the facial recognition program is preferentially called to identify the patient's eyes, and the code mosaic is used to hide the private information that can identify the patient, thereby Store, compute, and analyze it.
  • the central server receives the data and images uploaded by the terminal, and invokes the corresponding program unit for calculation and analysis. After the calculation is completed, it outputs the calculation results to the central database and the display and memory of the user terminal.
  • the output results of the prediction result output unit include the analog output of the image, the The output of the calculation result of the door field of view and the credible range of this value based on big data statistics.
  • the system administrator can set various parameters of the system through the central control module, including user personnel management, authorized project content, calling program unit selection and mode selection.
  • the modes include clinical application mode and scientific research mode;
  • the system administrator can retrieve, classify, delete, backup, edit and analyze the data stored on the server.
  • the user can select items after logging in. If "New Patient” is selected, the information input interface shown in Figure 5 will be entered, and the user will input the patient's information. After the series of analysis, the analysis results are shown in the result output interface as shown in Figure 6, and the glottis field of view is calculated to predict whether the patient is a difficult airway patient; if you select "My Patient", you will enter the patient data interface. Users can modify and delete patient data managed by themselves, and can further retrieve and classify patient data, as well as generate patient case report forms.

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Abstract

L'invention concerne un logiciel d'application informatique et un système de données de gestion des voies respiratoires pour la prédiction de difficultés au niveau des voies respiratoires, comprenant : un module d'application de personnel médical et un module de commande centrale. Chacun des modules d'application pour le personnel médical et de commande centrale comprend de multiples unités. L'invention est avantageuse en ce que : le logiciel est conçu sur la base de mécanismes endogènes contribuant à des difficultés au niveau des voies respiratoires, de sorte que la précision de prédiction est améliorée ; seuls quelques facteurs directs contribuant à des difficultés au niveau des voies respiratoires sont retenus, de sorte qu'un processus de détermination multi-factoriel complexe est éliminé ; des techniques de calcul en infographie sont utilisées pour effectuer une modélisation et une simulation, de façon à obtenir une haute précision de détection ; l'apprentissage automatique par intelligence artificielle est effectué en fonction de larges échantillons de données cliniques des difficultés au niveau des voies respiratoires et de données de caractéristiques faciales des voies respiratoires présentant des difficultés afin d'établir respectivement un programme de modèle de prédiction par intelligence artificielle des difficultés au niveau des voies respiratoires et un programme de modèle de reconnaissance faciale des voies respiratoires présentant des difficultés, de sorte que la performance de prédiction est également améliorée ; et une programmation de base de modèle de fonction et une fonction de gestion de mégadonnées sont utilisées, de sorte que des utilisateurs peuvent utiliser celles-ci aisément dans des applications cliniques correspondantes et un travail de recherche scientifique.
PCT/CN2021/128907 2020-11-10 2021-11-05 Logiciel d'application informatique et système de données de gestion des voies respiratoires pour la prédiction de difficultés au niveau des voies respiratoires WO2022100520A1 (fr)

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CN113069080B (zh) * 2021-03-22 2021-12-21 上海交通大学医学院附属第九人民医院 一种基于人工智能的困难气道评估方法及装置
CN113571088B (zh) * 2021-07-27 2023-10-03 上海交通大学医学院附属第九人民医院 一种基于深度学习声纹识别的困难气道评估方法及装置
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